pyflink.multimodal.expression.ImageExpressionAccessor.embedding

ImageExpressionAccessor.embedding(*, model: str = 'ViT-B/32', pretrained: str = 'openai', model_sharing: Optional[Literal['process', 'shared']] = None, concurrency: Optional[int] = None, batch_size: Optional[int] = None, num_gpus: Optional[float] = None, gpu_type: Optional[str] = None) → pyflink.table.expression.Expression

Compute image embeddings.

Equivalent to image_embedding(). See that function for full parameter details.

Parameters

• model – Embedding model name.

• pretrained – Pretrained weights identifier.

• model_sharing – Optional model sharing mode.

• concurrency – Optional execution concurrency for this operation. None uses the framework default.

• batch_size – Optional inference batch size.

• num_gpus – Optional number of GPUs requested for model inference.

• gpu_type – Optional GPU type requested for model inference.

Returns

A DataFrame expression that produces ARRAY<FLOAT> embedding vectors.